What question did this study set out to answer?

The primary aim is to validate a testing framework for integrating AI in legal contexts and assess LLMs for law enforcement tasks.

March 5, 2026Open Access

Method for Evaluating the Effectiveness of Large Language Models for Law Enforcement Purposes: Towards AI Standardization in Public Administration

Key Points

The primary aim is to validate a testing framework for integrating AI in legal contexts and assess LLMs for law enforcement tasks.
Developed a verification framework called 'Standardized Legal Test' for AI systems in legal environments.
Conducted a multi-factor analysis of LLM outputs, focusing on legal reasoning and accuracy.
Tested models using adapted legal cases to evaluate compliance with legality principles.
Established objective criteria for assessing AI systems prior to deployment in state information systems.
Identified limitations such as 'hallucinations' in LLM outputs affecting legal interpretation.
Proposed a method for standardized certification protocols to enhance AI integration in public administration.

Abstract

This research explores the creation of a verification framework for artificial intelligence (AI) systems integrated into the legal environment. The study focuses on the complex of social relations emerging from the integration of AI technologies into the mechanism of legal regulation and public administration, driven by the transformation of law enforcement technologies in the digital reality. The primary objective is to provide a theoretical substantiation and practical validation of the "Standardized Legal Test" designed to verify the analytical suitability of Large Language Models (LLMs) for law enforcement tasks. The research aims to establish a system of objective criteria to evaluate the compliance of automated systems with the principles of legality, legal logic, and legal certainty prior to their deployment within state information systems. The study is based on the experimental approbation of LLMs through the resolution of adapted legal cases. The methodology involves a multi-factor analysis of model outputs, focusing on legal reasoning, the accuracy of statutory interpretation, and the identification of "hallucinations." The novelty of the research lies in the development and testing of a universal legal testing format that is independent of specific AI architectures. This approach allows for the formalization of technological risk assessment within the legal domain. The author evaluates the universality of the developed method across several criteria, proposes current application scenarios, and provides a forecast for its evolution as a basis for the normative codification of AI system requirements. The findings are applicable to the design of state information systems and the implementation of AI assistants in public authorities. The study concludes that a transition is necessary from the ad hoc use of AI to the implementation of standardized certification protocols. The research predicts a transformation of legal drafting techniques toward the creation of machine-readable standards for algorithmic verification.

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